The design of radio-frequency power amplifiers (PAs) for communications applications often involves a trade-off between linearity and efficiency. Power amplifiers are typically most efficient when operated at or near the saturation point. However, the response of the amplifier at or near the point of saturation is non-linear. Generally speaking, when operating in the high-efficiency range, a power amplifier's response exhibits a nonlinear response and memory effects.
One way to improve a power amplifier's efficiency and its overall linearity is to digitally predistort the input to the power amplifier to compensate for the distortion introduced by the power amplifier. In effect, the input signal is adjusted in anticipation of the distortion to be introduced by the power amplifier, so that the output signal is largely free of distortion products. Generally, digital predistortion is applied to the signal at baseband frequencies, i.e., before the signal is upconverted to radio frequencies.
These techniques can be quite beneficial in improving the overall performance of a transmitter system, in terms of both linearity and efficiency. Furthermore, these techniques can be relatively inexpensive, due to the digital implementation of the predistorter. In fact, with the availability of these techniques, power amplifiers may be designed in view of more relaxed linearity requirements than would otherwise be permissible, thus potentially reducing the costs of the overall system.
Some techniques realize these advantages by accounting for memory effects, i.e., the dependence of an output signal on prior states of the input signal as well as on the present state. One problem associated with adding memory effects to conventional distortion models, however, is the extra instability added to the model parameter evaluation process due to the introduction of the memory model terms in the model. A fundamental source of this added instability is the high correlation among the data samples used in the parameter evaluations.